Clock Catalogue

Browse and filter every aging clock available in pyaging. Filter by any categorical column — data type, species, platform, model type, unit, tissue, last author, journal, and more; search by name, author, or notes; sort any column; toggle between table and card views; and click a clock to expand its full details.

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Clock name	Data type	Species	Predicts	Training target	Unit	Tissue	Platform	Population	Model type	N features	Year	Citations	Citations date	Last author	Journal	DOI	Notes	Preprocess	Postprocess	Reference values	Verified
altumage	DNA methylation	Homo sapiens	chronological age	chronological age	years	multi-tissue	Illumina 27K | Illumina 450K	all ages	deep neural network	20318	2022	145	2026-07-05	Ritambhara Singh	npj Aging	https://doi.org/10.1038/s41514-022-00085-y	Pan-tissue chronological-age predictor using a five-hidden-layer neural network and 20,318 CpGs shared across the 27K, 450K and EPIC manifests; the actual training data came from 27K and 450K datasets.	scale		True	By authors
bitage	transcriptomics	Caenorhabditis elegans	biological age	biological age	hours	whole organism	RNA-seq	Caenorhabditis elegans	elastic net regression	576	2021	173	2026-07-05	Björn Schumacher	Aging Cell	https://doi.org/10.1111/acel.13320	Binarized whole-organism C. elegans RNA-seq clock that estimates temporally rescaled biological age; the released linear predictor sums coefficients for genes binarized on plus a 103.55-hour intercept.	binarize			By authors
camilloh3k27ac	histone modification	Homo sapiens	chronological age	chronological age	years	multi-tissue	ChIP-seq	all ages	PCA + elastic net + ARD regression	1275	2025	4	2026-07-05	Ritambhara Singh	Science Advances	https://doi.org/10.1126/sciadv.adk9373	Pan-tissue chronological-age predictor trained on gene-level H3K27ac ChIP-seq enrichment; ElasticNet feature selection is followed by truncated-SVD PCA and automatic relevance determination regression.				By authors
camilloh3k27me3	histone modification	Homo sapiens	chronological age	chronological age	years	multi-tissue	ChIP-seq	all ages	PCA + elastic net + ARD regression	922	2025	4	2026-07-05	Ritambhara Singh	Science Advances	https://doi.org/10.1126/sciadv.adk9373	Pan-tissue chronological-age predictor trained on gene-level H3K27me3 ChIP-seq enrichment; ElasticNet feature selection is followed by truncated-SVD PCA and automatic relevance determination regression.				By authors
camilloh3k36me3	histone modification	Homo sapiens	chronological age	chronological age	years	multi-tissue	ChIP-seq	all ages	PCA + elastic net + ARD regression	870	2025	4	2026-07-05	Ritambhara Singh	Science Advances	https://doi.org/10.1126/sciadv.adk9373	Pan-tissue chronological-age predictor trained on gene-level H3K36me3 ChIP-seq enrichment; ElasticNet feature selection is followed by truncated-SVD PCA and automatic relevance determination regression.				By authors
camilloh3k4me1	histone modification	Homo sapiens	chronological age	chronological age	years	multi-tissue	ChIP-seq	all ages	PCA + elastic net + ARD regression	892	2025	4	2026-07-05	Ritambhara Singh	Science Advances	https://doi.org/10.1126/sciadv.adk9373	Pan-tissue chronological-age predictor trained on gene-level H3K4me1 ChIP-seq enrichment; ElasticNet feature selection is followed by truncated-SVD PCA and automatic relevance determination regression.				By authors
camilloh3k4me3	histone modification	Homo sapiens	chronological age	chronological age	years	multi-tissue	ChIP-seq	all ages	PCA + elastic net + ARD regression	1240	2025	4	2026-07-05	Ritambhara Singh	Science Advances	https://doi.org/10.1126/sciadv.adk9373	Pan-tissue chronological-age predictor trained on gene-level H3K4me3 ChIP-seq enrichment; ElasticNet feature selection is followed by truncated-SVD PCA and automatic relevance determination regression.				By authors
camilloh3k9ac	histone modification	Homo sapiens	chronological age	chronological age	years	multi-tissue	ChIP-seq	all ages	PCA + elastic net + ARD regression	102	2025	4	2026-07-05	Ritambhara Singh	Science Advances	https://doi.org/10.1126/sciadv.adk9373	Pan-tissue chronological-age predictor trained on gene-level H3K9ac ChIP-seq enrichment; ElasticNet feature selection is followed by truncated-SVD PCA and automatic relevance determination regression.				By authors
camilloh3k9me3	histone modification	Homo sapiens	chronological age	chronological age	years	multi-tissue	ChIP-seq	all ages	PCA + elastic net + ARD regression	341	2025	4	2026-07-05	Ritambhara Singh	Science Advances	https://doi.org/10.1126/sciadv.adk9373	Pan-tissue chronological-age predictor trained on gene-level H3K9me3 ChIP-seq enrichment; ElasticNet feature selection is followed by truncated-SVD PCA and automatic relevance determination regression.				By authors
camillopanhistone	histone modification	Homo sapiens	chronological age	chronological age	years	multi-tissue	ChIP-seq	all ages	PCA + elastic net + ARD regression	3739	2025	4	2026-07-05	Ritambhara Singh	Science Advances	https://doi.org/10.1126/sciadv.adk9373	Pan-tissue, pan-histone chronological-age predictor trained on gene-level ChIP-seq enrichment from seven histone modifications; ElasticNet feature selection is followed by truncated-SVD PCA and automatic relevance determination regression.				By authors
cpgptgrimage3	DNA methylation	Homo sapiens	biological age | mortality risk	mortality	years	whole blood	Illumina 450K	adults	Cox proportional hazards regression	24	2024	30	2026-07-05	Bo Wang	bioRxiv	https://doi.org/10.1101/2024.10.24.619766	CpGPTGrimAge3 implementation combining chronological age, GrimAge2 DNAm proxies, and CpGPT-predicted plasma-protein proxies in a Cox linear predictor that is calibrated to years.	scale	cox_to_years		By authors
cpgptpcgrimage3	DNA methylation	Homo sapiens	biological age | mortality risk	mortality	years	whole blood	Illumina 450K	adults	PCA + Cox regression	31	2024	30	2026-07-05	Bo Wang	bioRxiv	https://doi.org/10.1101/2024.10.24.619766	Principal-component CpGPTGrimAge3 implementation combining chronological age, GrimAge2 DNAm proxies, and CpGPT-predicted plasma-protein proxies; 30 proxy inputs are projected to 29 PCs, entered with age into a Cox linear predictor, and calibrated to years.	scale	cox_to_years		By authors
dunedinpace	DNA methylation	Homo sapiens	pace of aging	pace of aging	biological years per chronological year	whole blood	Illumina EPIC	adults	elastic net regression	20000	2022	967	2026-07-05	Terrie E. Moffitt	eLife	https://doi.org/10.7554/elife.73420	Whole-blood elastic-net pace-of-aging biomarker trained at age 45 against a 20-year longitudinal slope composite of 19 organ-system biomarkers. PyAging follows the official 20,000-probe quantile-normalization panel: 173 scoring CpGs plus 19,827 background probes.	quantile_normalization_with_gold_standard		True	By authors
han	DNA methylation	Homo sapiens	chronological age	chronological age	years	whole blood	Illumina 450K	all ages	linear regression	65	2020	102	2026-07-05	Wolfgang Wagner	BMC Biology	https://doi.org/10.1186/s12915-020-00807-2	Whole-blood 65-CpG multivariable linear age predictor selected for robust targeted measurement; it fits Horvath-transformed chronological age and inverse-transforms the return to years.		anti_log_linear		By authors
knight	DNA methylation	Homo sapiens	gestational age	gestational age	weeks	cord blood | neonatal blood spots	Illumina 27K | Illumina 450K	newborns	elastic net regression	148	2016	312	2026-07-05	Alicia K. Smith	Genome Biology	https://doi.org/10.1186/s13059-016-1068-z	Elastic-net DNA-methylation estimator of gestational age at birth trained across six cord-blood and neonatal blood-spot cohorts, using 148 CpGs shared across the 27K and 450K arrays.			True	By authors
leecontrol	DNA methylation	Homo sapiens	gestational age	gestational age	weeks	placenta	Illumina 450K | Illumina EPIC	pregnancies	elastic net regression	546	2019	170	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.102049	Control placental clock trained on placentas designated as controls, with known major placental pathology excluded.				By authors
leerefinedrobust	DNA methylation	Homo sapiens	gestational age	gestational age	weeks	placenta	Illumina 450K | Illumina EPIC	pregnancies	elastic net regression	395	2019	170	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.102049	Refined robust placental clock fitted within uncomplicated term pregnancies (gestational age >36 weeks) using the original RPC loci as candidates.				By authors
leerobust	DNA methylation	Homo sapiens	gestational age	gestational age	weeks	placenta	Illumina 450K | Illumina EPIC	pregnancies	elastic net regression	558	2019	170	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.102049	Robust placental clock trained across placentas with and without pregnancy complications and congenital abnormalities.				By authors
pasta	transcriptomics	Homo sapiens	transcriptomic age	age ordering	years	multi-tissue	RNA-seq | gene expression microarray	human, age unspecified	ridge logistic regression	8113	2025	1	2026-07-05	Christian G. Riedel	bioRxiv	https://doi.org/10.1101/2025.06.04.657785	Human Pasta age-shift classifier applied to within-sample rank-transformed expression. It converts a ridge-logistic older-versus-younger log-odds score to an age score.	median_fill_and_rank_normalization	scale_and_shift	True	By authors
pastamouse	transcriptomics	Mus musculus	transcriptomic age	age ordering	years	multi-tissue	RNA-seq | gene expression microarray	human, age unspecified	orthologue-transferred ridge logistic regression	1600	2025	1	2026-07-05	Christian G. Riedel	bioRxiv	https://doi.org/10.1101/2025.06.04.657785	Mouse application of the human Pasta model after mapping one-to-one orthologues, rank transformation, and median imputation of missing model genes.	median_fill_and_rank_normalization	scale_and_shift	True	By authors
pipekelasticnet	DNA methylation	Homo sapiens	chronological age	chronological age	years	multi-tissue	Illumina 27K | Illumina 450K | Illumina EPIC	all ages	elastic net regression	239	2022	2	2026-07-05	István Csabai	Journal of Mathematical Chemistry	https://doi.org/10.1007/s10910-022-01381-4	Pan-tissue, cross-platform elastic-net chronological-age clock trained on all eligible CpGs; 239 CpGs retained non-zero coefficients.		anti_log_linear		By authors
pipekfilteredh	DNA methylation	Homo sapiens	chronological age	chronological age	years	multi-tissue	Illumina 27K | Illumina 450K | Illumina EPIC	all ages	elastic net regression	272	2022	2	2026-07-05	István Csabai	Journal of Mathematical Chemistry	https://doi.org/10.1007/s10910-022-01381-4	Penalized refit restricted to the 308 original Horvath CpGs shared with the study probe set; 272 CpGs retained non-zero coefficients.		anti_log_linear		By authors
pipekretrainedh	DNA methylation	Homo sapiens	chronological age	chronological age	years	multi-tissue	Illumina 27K | Illumina 450K | Illumina EPIC	all ages	linear regression	308	2022	2	2026-07-05	István Csabai	Journal of Mathematical Chemistry	https://doi.org/10.1007/s10910-022-01381-4	Unpenalized refit of all 308 original Horvath CpGs shared across 27K, 450K, and EPIC data; unlike the other variants it was fit without cross-validation.		anti_log_linear		By authors
reg	transcriptomics	Homo sapiens	chronological age	chronological age	years	multi-tissue	RNA-seq | gene expression microarray	human, age unspecified	ridge regression	8113	2025	1	2026-07-05	Christian G. Riedel	bioRxiv	https://doi.org/10.1101/2025.06.04.657785	Baseline chronological-age regression model from the Pasta study, using rank-transformed multi-tissue human expression.	median_fill_and_rank_normalization	add_constant	True	By authors
stemtoc	DNA methylation	Homo sapiens	mitotic age	population doublings | chronological age	beta value	multi-tissue | cultured human cells | whole blood	Illumina 450K | Illumina EPIC	all ages	95th-percentile methylation aggregation	371	2024	24	2026-07-05	Andrew E. Teschendorff	Nature Communications	https://doi.org/10.1038/s41467-024-48649-8	Relative mitotic-age counter based on the 95th percentile across 371 in-vivo-filtered mitotic CpGs.	0.95 quantile		True	By authors
stoch	DNA methylation	Homo sapiens	chronological age	chronological age	years	sorted monocytes	Illumina 450K	adults	elastic net regression	353	2024	66	2026-07-05	Andrew E. Teschendorff	Nature Aging	https://doi.org/10.1038/s43587-024-00600-8	Stochastic chronological-age clock built from simulated methylation trajectories at the Horvath clock CpGs; it is a stochastic counterpart, not the original Horvath clock.				By authors
stocp	DNA methylation	Homo sapiens	chronological age	chronological age	years	sorted monocytes	Illumina 450K	adults	elastic net regression	513	2024	66	2026-07-05	Andrew E. Teschendorff	Nature Aging	https://doi.org/10.1038/s43587-024-00600-8	Stochastic chronological-age clock built from simulated methylation trajectories at PhenoAge CpGs; despite its CpG source, its fitted outcome and returned construct are chronological age, not PhenoAge.				By authors
stocz	DNA methylation	Homo sapiens	chronological age	chronological age	years	sorted monocytes	Illumina 450K	adults	elastic net regression	514	2024	66	2026-07-05	Andrew E. Teschendorff	Nature Aging	https://doi.org/10.1038/s43587-024-00600-8	Stochastic chronological-age clock built from simulated methylation trajectories at Zhang clock CpGs; it is a stochastic counterpart, not the original Zhang clock.				By authors
thompson	DNA methylation	Mus musculus	chronological age	chronological age	months	adipose tissue | blood | cerebellum | brain cortex | heart | kidney | liver | lung | skeletal muscle | spleen	RRBS	mice	elastic net regression	582	2018	236	2026-07-05	Matteo Pellegrini	Aging (Albany NY)	https://doi.org/10.18632/aging.101590	Full-lifespan multi-tissue mouse DNA-methylation clock fit by elastic net to RRBS CpG methylation across 1,147 samples from ten tissues and multiple strains; the 582-site all-CpG model estimates chronological age and detects intervention- and genotype-associated age acceleration.				By authors
abec	DNA methylation	Homo sapiens	chronological age	chronological age	years	whole blood	Illumina EPIC	adults	elastic net regression	1695	2020	21	2026-07-05	Jon Bohlin	BMC Genomics	https://doi.org/10.1186/s12864-020-07168-8	Adult Blood-based EPIC Clock trained by elastic-net regression of chronological age on whole-blood EPIC methylation in 1,592 MoBa-START adults aged 19–59 years.				Not yet
adbahadosingh	DNA methylation	Homo sapiens	late-onset Alzheimer’s disease	late-onset Alzheimer’s disease	probability	whole blood	Illumina EPIC	older adults	logistic regression	4	2021	33	2026-07-05	Uppala Radhakrishna	PLOS ONE	https://doi.org/10.1371/journal.pone.0248375	PyAging implements the paper’s conventional four-CpG logistic-regression equation and applies a sigmoid to return LOAD case probability; it does not implement the separate high-dimensional deep-learning classifiers also evaluated in the paper.		sigmoid		Not yet
bocklandt	DNA methylation	Homo sapiens	EDARADD methylation	chronological age	beta value	saliva	Illumina 27K	adults	single-CpG score	1	2011	1057	2026-07-05	Éric Vilain	PLoS ONE	https://doi.org/10.1371/journal.pone.0014821	Package-facing one-CpG identity score: pyaging returns raw cg09809672 methylation with coefficient 1 and zero intercept. The published saliva age regression instead uses EDARADD and NPTX2, including an EDARADD-squared basis term; that published age model is not implemented.				Not yet
bohlin	DNA methylation	Homo sapiens	gestational age	gestational age	weeks	cord blood	Illumina 450K	newborns	LASSO regression	251	2016	237	2026-07-05	Wenche Nystad	Genome Biology	https://doi.org/10.1186/s13059-016-1063-4	Official minimum-lambda variant of the Bohlin gestational-age LASSO: pyaging implements the 251-CpG lambda.min model and converts its day-scale output to weeks. The paper/package default one-standard-error variant uses 96 CpGs and has nearly identical predictive performance.		days_to_weeks		Not yet
cabec	DNA methylation	Homo sapiens	chronological age	chronological age	years	whole blood	Illumina 450K | Illumina EPIC	adults	elastic net regression	1892	2020	21	2026-07-05	Jon Bohlin	BMC Genomics	https://doi.org/10.1186/s12864-020-07168-8	Common Adult Blood-based EPIC Clock trained on the extended adult whole-blood dataset but restricted to autosomal CpGs shared by the Illumina 450K and EPIC arrays.				Not yet
cellpopage	DNA methylation	Homo sapiens	cell-population passage age	cell passage number	passages	cultured fibroblasts	Illumina EPIC	human cell cultures	elastic net regression	42	2024	6	2026-07-05	Ivana Bjedov	Genome Medicine	https://doi.org/10.1186/s13073-024-01349-w	CellPopAge is an elastic-net DNA-methylation clock using 42 selected CpGs to predict passage-based age of serially cultured adult primary human fibroblast populations and screen compounds that decelerate this measure.				Not yet
compil6	DNA methylation	Homo sapiens	interleukin-6	interleukin-6	unitless	whole blood	Illumina 450K	older adults	elastic net regression	35	2021	55	2026-07-05	Riccardo E. Marioni	The Journals of Gerontology: Series A	https://doi.org/10.1093/gerona/glab046	Thirty-five-CpG whole-blood proxy score for persistent IL-6-related inflammatory burden, fitted against covariate-adjusted normalized plasma IL-6.				Not yet
corticalclock	DNA methylation	Homo sapiens	chronological age	chronological age	years	brain cortex	Illumina 450K	human, age unspecified	elastic net regression	347	2020	206	2026-07-05	Jonathan Mill	Brain	https://doi.org/10.1093/brain/awaa334	Cortex-specific DNA-methylation chronological-age estimator trained by elastic net on 1,047 post-mortem cortical samples; its 347-CpG weighted score is back-transformed to years.		anti_log_linear	True	Not yet
ctsliver	DNA methylation	Homo sapiens	chronological age	chronological age	years	liver	Illumina EPIC	adults	LASSO regression	90	2024	25	2026-07-05	Andrew E. Teschendorff	Aging	https://doi.org/10.18632/aging.206184	LiverClock is a liver tissue-specific chronological-age clock trained by lasso on age-associated CpGs identified after adjustment for five estimated liver cell fractions; unlike HepClock, it is not hepatocyte-specific.				Not yet
cvdwesterman	DNA methylation	Homo sapiens	cardiovascular disease risk	cardiovascular disease	probability	whole blood	Illumina 450K	adults	elastic net Cox ensemble	235	2020	53	2026-07-05	José M. Ordovás	Journal of the American Heart Association	https://doi.org/10.1161/jaha.119.015299	Whole-blood DNA-methylation score for cardiovascular risk. The paper’s final cross-study learner stacks cohort-specific elastic-net Cox models; the packaged pyaging implementation is a 235-CpG linear score followed by a sigmoid.		sigmoid		Not yet
deconvolutebloodepicbcell	DNA methylation	Homo sapiens	B cell proportion	cell-type-specific methylation contrast	proportion	purified blood leukocytes	Illumina EPIC	adults	reference-based constrained deconvolution	600	2018	13	2026-07-05	Brock C. Christensen	Genome Biology	https://doi.org/10.1186/s13059-018-1448-7	Reference-based constrained deconvolution returning the B cell proportion from EPIC-array blood methylation. Pyaging uses the paper’s automatic 600-CpG top-hypermethylated/top-hypomethylated reference, not the paper’s preferred 450-CpG EPIC IDOL library.	fill_with_reference_means		True	Not yet
deconvolutebloodepiccd4tcell	DNA methylation	Homo sapiens	CD4+ T cell proportion	cell-type-specific methylation contrast	proportion	purified blood leukocytes	Illumina EPIC	adults	reference-based constrained deconvolution	600	2018	13	2026-07-05	Brock C. Christensen	Genome Biology	https://doi.org/10.1186/s13059-018-1448-7	Reference-based constrained deconvolution returning the CD4+ T cell proportion from EPIC-array blood methylation. Pyaging uses the paper’s automatic 600-CpG top-hypermethylated/top-hypomethylated reference, not the paper’s preferred 450-CpG EPIC IDOL library.	fill_with_reference_means		True	Not yet
deconvolutebloodepiccd8tcell	DNA methylation	Homo sapiens	CD8+ T cell proportion	cell-type-specific methylation contrast	proportion	purified blood leukocytes	Illumina EPIC	adults	reference-based constrained deconvolution	600	2018	13	2026-07-05	Brock C. Christensen	Genome Biology	https://doi.org/10.1186/s13059-018-1448-7	Reference-based constrained deconvolution returning the CD8+ T cell proportion from EPIC-array blood methylation. Pyaging uses the paper’s automatic 600-CpG top-hypermethylated/top-hypomethylated reference, not the paper’s preferred 450-CpG EPIC IDOL library.	fill_with_reference_means		True	Not yet
deconvolutebloodepicmonocyte	DNA methylation	Homo sapiens	monocyte proportion	cell-type-specific methylation contrast	proportion	purified blood leukocytes	Illumina EPIC	adults	reference-based constrained deconvolution	600	2018	13	2026-07-05	Brock C. Christensen	Genome Biology	https://doi.org/10.1186/s13059-018-1448-7	Reference-based constrained deconvolution returning the monocyte proportion from EPIC-array blood methylation. Pyaging uses the paper’s automatic 600-CpG top-hypermethylated/top-hypomethylated reference, not the paper’s preferred 450-CpG EPIC IDOL library.	fill_with_reference_means		True	Not yet
deconvolutebloodepicneutrophil	DNA methylation	Homo sapiens	neutrophil proportion	cell-type-specific methylation contrast	proportion	purified blood leukocytes	Illumina EPIC	adults	reference-based constrained deconvolution	600	2018	13	2026-07-05	Brock C. Christensen	Genome Biology	https://doi.org/10.1186/s13059-018-1448-7	Reference-based constrained deconvolution returning the neutrophil proportion from EPIC-array blood methylation. Pyaging uses the paper’s automatic 600-CpG top-hypermethylated/top-hypomethylated reference, not the paper’s preferred 450-CpG EPIC IDOL library.	fill_with_reference_means		True	Not yet
deconvolutebloodepicnkcell	DNA methylation	Homo sapiens	natural killer cell proportion	cell-type-specific methylation contrast	proportion	purified blood leukocytes	Illumina EPIC	adults	reference-based constrained deconvolution	600	2018	13	2026-07-05	Brock C. Christensen	Genome Biology	https://doi.org/10.1186/s13059-018-1448-7	Reference-based constrained deconvolution returning the natural killer (NK) cell proportion from EPIC-array blood methylation. Pyaging uses the paper’s automatic 600-CpG top-hypermethylated/top-hypomethylated reference, not the paper’s preferred 450-CpG EPIC IDOL library.	fill_with_reference_means		True	Not yet
depressionbarbu	DNA methylation	Homo sapiens	major depressive disorder	major depressive disorder	unitless	whole blood	Illumina EPIC	adults	elastic net regression	196	2021	93	2026-07-05	Andrew M. McIntosh	Molecular Psychiatry	https://doi.org/10.1038/s41380-020-0808-3	Blood methylation risk score for major depressive disorder built with penalised regression on genome-wide EPIC-array CpGs, trained on over 1,200 cases and 1,800 controls. Discriminates prevalent from incident MDD independently of polygenic risk, with a smoking-independent variant also derived.				Not yet
dnamfili	DNA methylation	Homo sapiens	frailty risk	frailty	unitless	whole blood	Illumina EPIC | Illumina 450K	older adults	LASSO regression	20	2022	22	2026-07-05	Hermann Brenner	Nature Communications	https://doi.org/10.1038/s41467-022-32893-x	Epigenetic frailty risk score (eFRS), a weighted 20-CpG whole-blood DNA-methylation score selected by LASSO from replicated frailty-associated loci.				Not yet
dnamfitage	DNA methylation	Homo sapiens	physical-fitness biological age	biological age	years	whole blood	Illumina 450K	adults	Klemera–Doubal composite	630	2023	99	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.204538	Sex-specific Klemera–Doubal biological-age composite combining DNAm gait speed, grip strength, VO2max, and DNAmGrimAge.			True	Not yet
dnamfitagegaitf	DNA methylation	Homo sapiens	gait speed	gait speed	meters per second	whole blood	Illumina 450K	adult women	LASSO regression	53	2023	99	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.204538	Female-specific blood DNAm gait-speed estimator without chronological age as an input.			True	Not yet
dnamfitagegaitm	DNA methylation	Homo sapiens	gait speed	gait speed	meters per second	whole blood	Illumina 450K	adult men	LASSO regression	59	2023	99	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.204538	Male-specific blood DNAm gait-speed estimator without chronological age as an input.			True	Not yet
dnamfitagegripf	DNA methylation	Homo sapiens	grip strength	grip strength	kilograms	whole blood	Illumina 450K	adult women	LASSO regression	91	2023	99	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.204538	Female-specific blood DNAm maximum-handgrip-strength estimator without chronological age as an input.			True	Not yet
dnamfitagegripm	DNA methylation	Homo sapiens	grip strength	grip strength	kilograms	whole blood	Illumina 450K	adult men	LASSO regression	93	2023	99	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.204538	Male-specific blood DNAm maximum-handgrip-strength estimator without chronological age as an input.			True	Not yet
dnamfitagevo2max	DNA methylation	Homo sapiens	VO2max	VO2max	milliliters per kilogram per minute	whole blood	Illumina 450K	adults	LASSO regression	41	2023	99	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.204538	Joint-sex blood DNAm estimator of maximal oxygen uptake; 40 CpGs plus chronological age are packaged as 41 inputs.			True	Not yet
dnamic	DNA methylation	Homo sapiens	intrinsic capacity	intrinsic capacity	unitless	whole blood	Illumina EPIC	older adults	elastic net regression	91	2025	34	2026-07-05	David Furman	Nature Aging	https://doi.org/10.1038/s43587-025-00883-5	Whole-blood DNA-methylation predictor of intrinsic capacity, an average score across cognition, locomotion, psychological, sensory, and vitality domains; trained with tenfold cross-validated elastic net on INSPIRE-T and returning higher values for better capacity.				Not yet
dnamphenoage	DNA methylation	Homo sapiens	phenotypic age	phenotypic age	years	whole blood	Illumina 27K | Illumina 450K | Illumina EPIC	adults	elastic net regression	513	2018	3594	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.101414	Blood DNA-methylation estimator trained by elastic net on 513 CpGs common to the 27K, 450K and EPIC arrays to reproduce a mortality-derived clinical Phenotypic Age.				Not yet
dnamstress	DNA methylation	Homo sapiens	stress exposure	stress exposure	unitless	whole blood	Illumina EPIC	adults	bootstrap-stabilized elastic net regression	211	2023	27	2026-07-05	Falk W. Lohoff	Biological Psychiatry	https://doi.org/10.1016/j.biopsych.2022.06.036	Whole-blood methylation score (MS stress) derived as a 211-CpG proxy for a composite of 13 stress-related measures.				Not yet
dnamtl	DNA methylation	Homo sapiens	leukocyte telomere length	leukocyte telomere length	kilobases	whole blood	Illumina 450K | Illumina EPIC	adults	elastic net regression	140	2019	461	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.102173	Elastic-net blood DNA-methylation estimator of measured leukocyte telomere length, using 140 CpGs shared by the Illumina 450K and EPIC arrays and returning kilobases.				Not yet
downsyndrome	DNA methylation	Homo sapiens	Down syndrome methylation score	not applicable	unitless	neonatal blood spots	Illumina EPIC	newborns	weighted linear score	652	2021	62	2026-07-05	Adam J. de Smith	Nature Communications	https://doi.org/10.1038/s41467-021-21064-z	Implementation-derived Down-syndrome-associated methylation projection score: pyaging computes a zero-intercept weighted sum of 652 neonatal blood-spot beta values using the paper’s autosomal EWAS beta_overall effect estimates. The paper presents an EWAS, not a trained or validated Down syndrome classifier.				Not yet
dunedinpoam38	DNA methylation	Homo sapiens	pace of aging	pace of aging	biological years per chronological year	whole blood	Illumina 450K	adults	elastic net regression	46	2020	666	2026-07-05	Terrie E. Moffitt	eLife	https://doi.org/10.7554/elife.54870	Whole-blood elastic-net estimator of the rate of biological aging, trained at age 38 against a longitudinal 18-biomarker Pace-of-Aging composite measured over ages 26–38.				Not yet
eabec	DNA methylation	Homo sapiens	chronological age	chronological age	years	whole blood	Illumina EPIC	adults	elastic net regression	1791	2020	21	2026-07-05	Jon Bohlin	BMC Genomics	https://doi.org/10.1186/s12864-020-07168-8	Extended Adult Blood-based EPIC Clock trained by elastic-net regression on combined MoBa-START and GEO adult whole-blood EPIC methylation data.				Not yet
encen100	DNA methylation	Homo sapiens	chronological age	chronological age	years	whole blood | saliva | buccal epithelium	Illumina 450K | Illumina EPIC	centenarians	elastic net regression	198	2023	45	2026-07-05	Steve Horvath	GeroScience	https://doi.org/10.1007/s11357-023-00731-7	Elastic-net DNAm-age clock trained only in 184 centenarians aged 100–115; the authors advise against routine use but identify possible utility for evaluating supercentenarians.				Not yet
encen40	DNA methylation	Homo sapiens	chronological age	chronological age	years	whole blood | saliva | buccal epithelium	Illumina 450K | Illumina EPIC	older adults	elastic net regression	559	2023	45	2026-07-05	Steve Horvath	GeroScience	https://doi.org/10.1007/s11357-023-00731-7	Elastic-net DNAm-age clock trained in 7,039 people aged 40–115, including centenarians, to reduce extreme-old-age underestimation.				Not yet
ensembleagehumanmouse	DNA methylation	Homo sapiens and Mus musculus	relative age	relative age	relative age	multi-tissue | whole blood	mammalian methylation array	humans and mice	elastic net regression	100	2025	3	2026-07-05	Steve Horvath	GeroScience	https://doi.org/10.1007/s11357-025-01808-1	Cross-species static EnsembleAge model trained on merged human and mouse methylation data; age is normalized by species maximum lifespan.				Not yet
ensembleagestatic	DNA methylation	Mus musculus	intervention-responsive epigenetic age	intervention-responsive epigenetic age	years	multi-tissue	Horvath MammalMethylChip40 | Horvath MammalMethylChip320	mice	elastic net regression	288	2025	3	2026-07-05	Steve Horvath	GeroScience	https://doi.org/10.1007/s11357-025-01808-1	Single elastic-net static predictor of the median EnsembleAge.Dynamic calibrated age, trained on perturbed MethylGauge mice.				Not yet
ensembleagestatictop	DNA methylation	Mus musculus	intervention-responsive epigenetic age	intervention-responsive epigenetic age	years	multi-tissue	Horvath MammalMethylChip40 | Horvath MammalMethylChip320	mice	elastic net regression	431	2025	3	2026-07-05	Steve Horvath	GeroScience	https://doi.org/10.1007/s11357-025-01808-1	Static.Top is a single elastic-net predictor of the calibrated age from the most intervention-responsive constituent clock, trained on perturbed MethylGauge mice.				Not yet
epicga	DNA methylation	Homo sapiens	gestational age	gestational age	days	cord blood	Illumina EPIC	newborns	LASSO regression	176	2021	54	2026-07-05	Jon Bohlin	Clinical Epigenetics	https://doi.org/10.1186/s13148-021-01055-z	LASSO predictor of ultrasound-estimated gestational age in days from umbilical cord-blood DNA methylation, trained in 755 non-ART START newborns.		days_to_weeks		Not yet
epicmithyper	DNA methylation	Homo sapiens	mitotic age	replicative history	proportion	B cells	Illumina 450K | Illumina EPIC	human, age unspecified	mean methylation aggregation	184	2020	104	2026-07-05	José I. Martín-Subero	Nature Cancer	https://doi.org/10.1038/s43018-020-00131-2	Hypermethylation component of epiCMIT: a 184-CpG score ranging from 0 to 1 that tracks low-to-high relative proliferative history in normal and neoplastic B cells.	mean		True	Not yet
epicmithypo	DNA methylation	Homo sapiens	mitotic age	replicative history	proportion	B cells	Illumina 450K | Illumina EPIC	human, age unspecified	complement of mean methylation	1164	2020	104	2026-07-05	José I. Martín-Subero	Nature Cancer	https://doi.org/10.1038/s43018-020-00131-2	Hypomethylation component of epiCMIT: a 1,164-CpG score ranging from 0 to 1 that tracks low-to-high relative proliferative history in normal and neoplastic B cells.	mean		True	Not yet
epitoc1	DNA methylation	Homo sapiens	mitotic age	chronological age	beta value	multi-tissue | whole blood	Illumina 450K	all ages	mean methylation aggregation	385	2016	357	2026-07-05	Andrew E. Teschendorff	Genome Biology	https://doi.org/10.1186/s13059-016-1064-3	Relative mitotic-age score equal to the mean beta value across 385 polycomb-target promoter CpGs; it is not an absolute division count.	mean		True	Not yet
epitoc2	DNA methylation	Homo sapiens	mitotic age	chronological age	cell divisions per stem cell	whole blood	Illumina 450K	adults	dynamic methylation transmission model	163	2020	155	2026-07-05	Andrew E. Teschendorff	Genome Medicine	https://doi.org/10.1186/s13073-020-00752-3	Dynamic methylation-transmission model returning total cumulative stem-cell divisions per stem cell; an intrinsic rate additionally requires chronological age but is not this implementation’s returned value.	nan_to_zero		True	Not yet
epitoc3	DNA methylation	Homo sapiens	mitotic age	population doublings	cell divisions per stem cell	cultured primary human cells | whole blood | multi-tissue | cord blood	Illumina 450K | Illumina EPIC	all ages	dynamic methylation transmission model	170	2020	155	2026-07-05	Andrew E. Teschendorff	Genome Medicine	https://doi.org/10.1186/s13073-020-00752-3	Code-defined 170-CpG extension of the dynamic mitotic model. Official EpiMitClocks data show that all 170 sites are a subset of the 371 stemTOC vivo-mitCpGs derived from fetal/neonatal references, six normal proliferating cell lines, and three adult whole-blood cohorts. The assigned 2020 dynamic-model paper does not name or define epiTOC3.	nan_to_zero		True	Not yet
garagnani	DNA methylation	Homo sapiens	ELOVL2 methylation	not applicable	beta value	whole blood	Illumina 450K	all ages	single-CpG score	1	2012	500	2026-07-05	Claudio Franceschi	Aging Cell	https://doi.org/10.1111/acel.12005	The source study identified age-associated ELOVL2 methylation, but did not publish a one-CpG age equation. Pyaging returns the raw cg16867657 methylation beta value using coefficient 1 and zero intercept; it does not return calibrated chronological age.				Not yet
gliasin	DNA methylation	Homo sapiens	chronological age	chronological age	years	brain cortex	Illumina 450K	adults	elastic net regression	220	2024	25	2026-07-05	Andrew E. Teschendorff	Aging	https://doi.org/10.18632/aging.206184	Glia-Sin is a glia semi-intrinsic chronological-age clock: elastic-net regression was restricted to glia age-DMCTs but fitted to methylation values not adjusted for brain cell fractions.				Not yet
grimage	DNA methylation	Homo sapiens	mortality risk	mortality	years	whole blood	Illumina 450K	adults	two-stage elastic net + Cox regression	1032	2019	2610	2026-07-05	Steve Horvath	Aging (Albany NY)	https://doi.org/10.18632/aging.101684	Age-calibrated mortality-risk estimator built in two stages from DNAm surrogates for plasma proteins and smoking pack-years, chronological age, and sex.		cox_to_years	True	Not yet
grimage2	DNA methylation	Homo sapiens	mortality risk	mortality	years	whole blood	Illumina 450K	older adults	elastic net Cox regression	1032	2022	291	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.204434	Mortality-risk epigenetic clock combining ten blood DNAm surrogate biomarkers with chronological age and sex; the Cox linear predictor is calibrated to an age-like value in years.		cox_to_years	True	Not yet
grimage2adm	DNA methylation	Homo sapiens	adrenomedullin	adrenomedullin	picograms per milliliter	whole blood	Illumina 450K	older adults	elastic net regression	187	2022	291	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.204434	ADM is a vasodilator peptide hormone; DNAm ADM is an inherited GrimAge plasma-protein surrogate.		cox_to_years	True	Not yet
grimage2b2m	DNA methylation	Homo sapiens	beta-2-microglobulin	beta-2-microglobulin	picograms per milliliter	whole blood	Illumina 450K	older adults	elastic net regression	92	2022	291	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.204434	B2M is linked to kidney function, cardiovascular disease and inflammation; DNAm B2M is a GrimAge component.		cox_to_years	True	Not yet
grimage2cystatinc	DNA methylation	Homo sapiens	cystatin C	cystatin C	picograms per milliliter	whole blood	Illumina 450K	older adults	elastic net regression	88	2022	291	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.204434	Cystatin C is a kidney-function biomarker; DNAm Cystatin C is a GrimAge component.		cox_to_years	True	Not yet
grimage2gdf15	DNA methylation	Homo sapiens	GDF-15	GDF-15	picograms per milliliter	whole blood	Illumina 450K	older adults	elastic net regression	138	2022	291	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.204434	GDF-15 is implicated in aging and mitochondrial dysfunction; DNAm GDF-15 is a GrimAge component.		cox_to_years	True	Not yet
grimage2leptin	DNA methylation	Homo sapiens	leptin	leptin	picograms per milliliter	whole blood	Illumina 450K	older adults	elastic net regression	187	2022	291	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.204434	Leptin is an adipose-derived hormone regulating energy balance; DNAm leptin is a GrimAge component.		cox_to_years	True	Not yet
grimage2loga1c	DNA methylation	Homo sapiens	hemoglobin A1c	hemoglobin A1c	natural-log percent	whole blood	Illumina 450K	older adults	elastic net regression	87	2022	291	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.204434	DNAm logA1C estimates the natural logarithm of winsorized hemoglobin A1C percentage and was newly added to GrimAge2.		cox_to_years	True	Not yet
grimage2logcrp	DNA methylation	Homo sapiens	C-reactive protein	C-reactive protein	natural-log milligrams per liter	whole blood	Illumina 450K	older adults	elastic net regression	132	2022	291	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.204434	DNAm logCRP estimates the natural logarithm of winsorized high-sensitivity CRP concentration and was newly added to GrimAge2.		cox_to_years	True	Not yet
grimage2packyrs	DNA methylation	Homo sapiens	smoking exposure	smoking exposure	pack-years	whole blood	Illumina 450K	older adults	elastic net regression	173	2022	291	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.204434	DNAm PACKYRS is a methylation surrogate for cumulative smoking exposure and a GrimAge component.		cox_to_years	True	Not yet
grimage2pai1	DNA methylation	Homo sapiens	PAI-1	PAI-1	picograms per milliliter	whole blood	Illumina 450K	older adults	elastic net regression	211	2022	291	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.204434	PAI-1 is linked to inflammation and metabolic conditions; DNAm PAI-1 is a GrimAge component.			True	Not yet
grimage2timp1	DNA methylation	Homo sapiens	TIMP-1	TIMP-1	picograms per milliliter	whole blood	Illumina 450K	older adults	elastic net regression	43	2022	291	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.204434	TIMP-1 inhibits metalloproteinases and has proliferative/anti-apoptotic roles; DNAm TIMP-1 is a GrimAge component.		cox_to_years	True	Not yet
hannum	DNA methylation	Homo sapiens	chronological age	chronological age	years	whole blood	Illumina 450K	adults	elastic net regression	71	2013	4501	2026-07-05	Kang Zhang	Molecular Cell	https://doi.org/10.1016/j.molcel.2012.10.016	Whole-blood elastic-net predictor of chronological age from 71 CpG methylation fractions, derived in a 482-person primary cohort and validated in 174 independent participants.				Not yet
hep	DNA methylation	Homo sapiens	chronological age	chronological age	years	liver	Illumina EPIC	adults	LASSO regression	70	2024	25	2026-07-05	Andrew E. Teschendorff	Aging	https://doi.org/10.18632/aging.206184	HepClock is a hepatocyte-specific chronological-age clock trained by lasso on hepatocyte age-DMCTs identified with CellDMC after estimating five liver cell fractions.				Not yet
hepatoxu	DNA methylation	Homo sapiens	hepatocellular carcinoma	hepatocellular carcinoma	unitless	plasma cell-free DNA	targeted bisulfite sequencing	adults	feature-selected logistic regression	10	2017	884	2026-07-05	Kang Zhang	Nature Materials	https://doi.org/10.1038/nmat4997	Ten-marker plasma cfDNA methylation logistic model producing the combined HCC diagnosis score (cd-score); this packaged model does not implement the separate eight-marker prognosis score.				Not yet
horvath2013	DNA methylation	Homo sapiens	chronological age	chronological age	years	multi-tissue	Illumina 27K | Illumina 450K	all ages	elastic net regression	353	2013	7318	2026-07-05	Steve Horvath	Genome Biology	https://doi.org/10.1186/gb-2013-14-10-r115	Pan-tissue DNAm-age predictor fitted by elastic net to a transformed chronological-age outcome and returned to the year scale; it uses 353 CpGs shared between the 27K and 450K arrays.		anti_log_linear	True	Not yet
hrsinchphenoage	DNA methylation	Homo sapiens	phenotypic age	phenotypic age	years	whole blood	Illumina 450K | Illumina EPIC	adults	weighted linear score	959	2022	497	2026-07-05	Morgan E. Levine	Nature Aging	https://doi.org/10.1038/s43587-022-00248-2	CpG-weighted HRS/InCHIANTI retraining of DNAm PhenoAge produced during the PC-clocks work; this implementation is not a principal-component clock.				Not yet
hypoclock	DNA methylation	Homo sapiens	mitotic age	not applicable	beta value	multi-tissue	Illumina 450K	human, age unspecified	mean aggregation	678	2020	452	2026-07-05	Andrew E. Teschendorff	Genome Medicine	https://doi.org/10.1186/s13073-020-00752-3	Pyaging returns an inverted HypoClock burden score, 1 minus the mean beta value across 678 solo-WCGW CpGs; higher values therefore indicate deeper PMD hypomethylation. The assigned 2018 paper is the biological precursor, while the named 678-site implementation is from 2020.	mean	one_minus	True	Not yet
intrinclock	DNA methylation	Homo sapiens	chronological age	chronological age	years	multi-tissue	Illumina 450K | Illumina EPIC	all ages	two-stage elastic net regression	380	2024	53	2026-07-05	Eric Verdin	Communications Biology	https://doi.org/10.1038/s42003-024-06609-4	Multi-tissue chronological-age clock designed by excluding CpGs associated with CD8+ T-cell differentiation, then fitting two sequential elastic-net models so predictions remain stable across immune-cell composition. The article reports 381 CpGs; the official lambda.min model and this implementation both use the same 380 non-zero CpG inputs.		anti_log_linear		Not yet
lin	DNA methylation	Homo sapiens	chronological age	chronological age	years	whole blood	Illumina 27K | Illumina 450K	adults	linear regression	99	2016	256	2026-07-05	Wolfgang Wagner	Aging	https://doi.org/10.18632/aging.100908	Whole-blood 99-CpG multivariate age estimator trained to predict chronological age; age acceleration from the model was secondarily tested for association with all-cause mortality.				Not yet
mammalian1	DNA methylation	multiple species	chronological age	chronological age	years	multi-tissue	Horvath MammalMethylChip40	multiple mammalian species	elastic net regression	335	2023	390	2026-07-05	Steve Horvath	Nature Aging	https://doi.org/10.1038/s43587-023-00462-6	Universal pan-mammalian clock 1: elastic-net regression of log-transformed chronological age on conserved mammalian-array CpGs, back-transformed to years.		anti_logp2		Not yet
mammalian2	DNA methylation	multiple species	chronological age	relative age	years	multi-tissue	Horvath MammalMethylChip40	multiple mammalian species	elastic net regression	2572	2023	390	2026-07-05	Steve Horvath	Nature Aging	https://doi.org/10.1038/s43587-023-00462-6	Universal pan-mammalian clock 2 fits relative age (age divided by species maximum lifespan) and the author inverse transformation returns species-adjusted chronological age in years.		mammalian2	True	Not yet
mammalian3	DNA methylation	multiple species	chronological age	chronological age	years	multi-tissue	Horvath MammalMethylChip40	multiple mammalian species	elastic net regression	2467	2023	390	2026-07-05	Steve Horvath	Nature Aging	https://doi.org/10.1038/s43587-023-00462-6	Universal pan-mammalian clock 3 fits a log-linear age transformation based on gestation and sexual maturity and returns chronological age in years.		mammalian3	True	Not yet
mammalianblood2	DNA methylation	multiple species	chronological age	relative age	years	blood	Horvath MammalMethylChip40	multiple mammalian species	elastic net regression	2257	2023	390	2026-07-05	Steve Horvath	Nature Aging	https://doi.org/10.1038/s43587-023-00462-6	Blood-specific universal clock 2 fits relative age and returns species-adjusted chronological age in years after the maximum-lifespan inverse transformation.		mammalian2	True	Not yet
mammalianblood3	DNA methylation	multiple species	chronological age	chronological age	years	blood	Horvath MammalMethylChip40	multiple mammalian species	elastic net regression	2097	2023	390	2026-07-05	Steve Horvath	Nature Aging	https://doi.org/10.1038/s43587-023-00462-6	Blood-specific universal clock 3 fits the gestation/maturity-based log-linear age transformation and returns chronological age in years.		mammalian3	True	Not yet
mammalianfemale	DNA methylation	multiple species	sex	sex	probability	multi-tissue	mammalian methylation array	multiple mammalian species	elastic net regression	101	2023	5	2026-07-05	Steve Horvath	bioRxiv (Cold Spring Harbor Laboratory)	https://doi.org/10.1101/2023.11.02.565286	Pan-mammalian elastic-net sex classifier based on conserved CpG methylation; the returned value is the probability that a sample is female.		sigmoid		Not yet
mammalianlifespan	DNA methylation	multiple species	species maximum lifespan	species maximum lifespan	years	multi-tissue	Horvath MammalMethylChip40	multiple mammalian species	elastic net regression	152	2024	5	2026-07-05	Steve Horvath	Science Advances	https://doi.org/10.1126/sciadv.adm7273	Pan-mammalian tissue-agnostic elastic-net predictor fitted to log species maximum life span from conserved CpG methylation; pyaging exponentiates the linear output to years.		anti_log	True	Not yet
mammalianskin2	DNA methylation	multiple species	chronological age	relative age	years	skin	Horvath MammalMethylChip40	multiple mammalian species	elastic net regression	2240	2023	390	2026-07-05	Steve Horvath	Nature Aging	https://doi.org/10.1038/s43587-023-00462-6	Skin-specific universal clock 2 fits relative age and returns species-adjusted chronological age in years after the maximum-lifespan inverse transformation.		mammalian2	True	Not yet
mammalianskin3	DNA methylation	multiple species	chronological age	chronological age	years	skin	Horvath MammalMethylChip40	multiple mammalian species	elastic net regression	2055	2023	390	2026-07-05	Steve Horvath	Nature Aging	https://doi.org/10.1038/s43587-023-00462-6	Skin-specific universal clock 3 fits the gestation/maturity-based log-linear age transformation and returns chronological age in years.		mammalian3	True	Not yet
mayne	DNA methylation	Homo sapiens	gestational age	gestational age	weeks	placenta	Illumina 27K | Illumina 450K	pregnancies	elastic net regression	62	2017	150	2026-07-05	Tina Bianco‐Miotto	Epigenomics	https://doi.org/10.2217/epi-2016-0103	Placental elastic-net clock trained on pooled healthy human placenta methylation datasets; 62 selected CpGs predict gestational age and were used to test age acceleration in early-onset preeclampsia.				Not yet
mccartneyalcohol	DNA methylation	Homo sapiens	alcohol consumption	alcohol consumption	units per week	whole blood	Illumina EPIC	adults	LASSO regression	450	2018	301	2026-07-05	Riccardo E. Marioni	Genome Biology	https://doi.org/10.1186/s13059-018-1514-1	Whole-blood DNAm LASSO score for alcohol consumption, trained in Generation Scotland on an age-, sex-, and ancestry-adjusted phenotype residual and evaluated out of sample in LBC1936.				Not yet
mccartneybmi	DNA methylation	Homo sapiens	body mass index	body mass index	unitless	whole blood	Illumina EPIC	adults	LASSO regression	1109	2018	301	2026-07-05	Riccardo E. Marioni	Genome Biology	https://doi.org/10.1186/s13059-018-1514-1	Whole-blood DNAm LASSO score for body mass index (BMI), trained in Generation Scotland on an age-, sex-, and ancestry-adjusted phenotype residual and evaluated out of sample in LBC1936.		sigmoid		Not yet
mccartneybodyfat	DNA methylation	Homo sapiens	body fat percentage	body fat percentage	unitless	whole blood	Illumina EPIC	adults	LASSO regression	968	2018	301	2026-07-05	Riccardo E. Marioni	Genome Biology	https://doi.org/10.1186/s13059-018-1514-1	Whole-blood DNAm LASSO score for body fat percentage, trained in Generation Scotland on an age-, sex-, and ancestry-adjusted phenotype residual and evaluated out of sample in LBC1936.		sigmoid		Not yet
mccartneyeducation	DNA methylation	Homo sapiens	educational attainment	educational attainment	unitless	whole blood	Illumina EPIC	adults	LASSO regression	373	2018	301	2026-07-05	Riccardo E. Marioni	Genome Biology	https://doi.org/10.1186/s13059-018-1514-1	Whole-blood DNAm LASSO score for educational attainment, trained in Generation Scotland on an age-, sex-, and ancestry-adjusted phenotype residual and evaluated out of sample in LBC1936.		sigmoid		Not yet
mccartneyhdlcholesterol	DNA methylation	Homo sapiens	HDL cholesterol	HDL cholesterol	unitless	whole blood	Illumina EPIC	adults	LASSO regression	737	2018	301	2026-07-05	Riccardo E. Marioni	Genome Biology	https://doi.org/10.1186/s13059-018-1514-1	Whole-blood DNAm LASSO score for HDL cholesterol, trained in Generation Scotland on an age-, sex-, and ancestry-adjusted phenotype residual and evaluated out of sample in LBC1936.		sigmoid		Not yet
mccartneyldlcholesterol	DNA methylation	Homo sapiens	LDL cholesterol	LDL cholesterol	unitless	whole blood	Illumina EPIC	adults	LASSO regression	233	2018	301	2026-07-05	Riccardo E. Marioni	Genome Biology	https://doi.org/10.1186/s13059-018-1514-1	Whole-blood DNAm LASSO score for LDL with remnant cholesterol, trained in Generation Scotland on an age-, sex-, and ancestry-adjusted phenotype residual and evaluated out of sample in LBC1936.		sigmoid		Not yet
mccartneysmoking	DNA methylation	Homo sapiens	smoking exposure	smoking exposure	pack-years	whole blood	Illumina EPIC	adults	LASSO regression	233	2018	301	2026-07-05	Riccardo E. Marioni	Genome Biology	https://doi.org/10.1186/s13059-018-1514-1	Whole-blood DNAm LASSO score for smoking exposure (pack-years), trained in Generation Scotland on an age-, sex-, and ancestry-adjusted phenotype residual and evaluated out of sample in LBC1936.				Not yet
mccartneytotalcholesterol	DNA methylation	Homo sapiens	total cholesterol	total cholesterol	unitless	whole blood	Illumina EPIC	adults	LASSO regression	204	2018	301	2026-07-05	Riccardo E. Marioni	Genome Biology	https://doi.org/10.1186/s13059-018-1514-1	Whole-blood DNAm LASSO score for total cholesterol, trained in Generation Scotland on an age-, sex-, and ancestry-adjusted phenotype residual and evaluated out of sample in LBC1936.		sigmoid		Not yet
mccartneytotalhdlratio	DNA methylation	Homo sapiens	total-to-HDL cholesterol ratio	total-to-HDL cholesterol ratio	ratio	whole blood	Illumina EPIC	adults	LASSO regression	412	2018	301	2026-07-05	Riccardo E. Marioni	Genome Biology	https://doi.org/10.1186/s13059-018-1514-1	Whole-blood DNAm LASSO score for total-to-HDL cholesterol ratio, trained in Generation Scotland on an age-, sex-, and ancestry-adjusted phenotype residual and evaluated out of sample in LBC1936.				Not yet
mccartneywhr	DNA methylation	Homo sapiens	waist-to-hip ratio	waist-to-hip ratio	ratio	whole blood	Illumina EPIC	adults	LASSO regression	226	2018	301	2026-07-05	Riccardo E. Marioni	Genome Biology	https://doi.org/10.1186/s13059-018-1514-1	Whole-blood DNAm LASSO score for waist-to-hip ratio, trained in Generation Scotland on an age-, sex-, and ancestry-adjusted phenotype residual and evaluated out of sample in LBC1936.				Not yet
meer	DNA methylation	Mus musculus	chronological age	chronological age	days	multi-tissue	RRBS	mice	elastic net regression	435	2018	203	2026-07-05	Vadim N. Gladyshev	eLife	https://doi.org/10.7554/elife.40675	Whole Lifespan Multi-Tissue (WLMT) mouse clock trained by elastic net on RRBS methylation percentages from untreated wild-type C57BL/6 samples.				Not yet
neusin	DNA methylation	Homo sapiens	chronological age	chronological age	years	brain cortex	Illumina 450K	adults	elastic net regression	672	2024	25	2026-07-05	Andrew E. Teschendorff	Aging	https://doi.org/10.18632/aging.206184	Neu-Sin is a neuron semi-intrinsic chronological-age clock: elastic-net regression was restricted to neuron age-DMCTs but fitted to methylation values not adjusted for brain cell fractions.				Not yet
ocampoatac1	chromatin accessibility	Homo sapiens	chronological age	chronological age	years	peripheral blood mononuclear cells	ATAC-seq	adults	elastic net regression	228	2023	49	2026-07-05	Alejandro Ocampo	GeroScience	https://doi.org/10.1007/s11357-023-00986-0	Published final ATAC-clock coefficient-table implementation using 228 open chromatin regions from the 80,400-region input peak set.	tpm_norm_log1p			Not yet
ocampoatac2	chromatin accessibility	Homo sapiens	chronological age	chronological age	years	peripheral blood mononuclear cells	ATAC-seq	adults	elastic net regression	380	2023	49	2026-07-05	Alejandro Ocampo	GeroScience	https://doi.org/10.1007/s11357-023-00986-0	Alternate packaged implementation loaded from the authors’ GitHub final_coefs.tsv; it represents the same uncorrected final ATAC-clock target, not a deployable cell-composition-corrected clock.	tpm_norm_log1p			Not yet
pcdnamtl	DNA methylation	Homo sapiens	leukocyte telomere length	DNAmTL output	base pairs	whole blood	Illumina 450K	adults	PCA + elastic net regression	78464	2022	497	2026-07-05	Morgan E. Levine	Nature Aging	https://doi.org/10.1038/s43587-022-00248-2	Principal-component proxy trained to reproduce the original DNAmTL clock score; the implemented returned score is on the DNAmTL base-pair scale.			True	Not yet
pcgrimage	DNA methylation	Homo sapiens	mortality risk	DNAm GrimAge output	years	whole blood	Illumina 450K	adults	PCA + elastic net regression	78466	2022	497	2026-07-05	Morgan E. Levine	Nature Aging	https://doi.org/10.1038/s43587-022-00248-2	Principal-component proxy trained to reproduce the original DNAm GrimAge score; age and sex are additional model inputs.			True	Not yet
pchannum	DNA methylation	Homo sapiens	chronological age	Hannum clock output	years	whole blood	Illumina 450K	adults	PCA + elastic net regression	78464	2022	497	2026-07-05	Morgan E. Levine	Nature Aging	https://doi.org/10.1038/s43587-022-00248-2	Principal-component proxy trained to reproduce the original Hannum whole-blood age-clock score.			True	Not yet
pchorvath2013	DNA methylation	Homo sapiens	chronological age	Horvath clock output	years	multi-tissue	Illumina 450K | Illumina EPIC	all ages	PCA + elastic net regression	78464	2022	497	2026-07-05	Morgan E. Levine	Nature Aging	https://doi.org/10.1038/s43587-022-00248-2	Principal-component proxy of the 2013 Horvath pan-tissue clock, trained against the original clock score using substituted multi-tissue datasets.		anti_log_linear	True	Not yet
pcphenoage	DNA methylation	Homo sapiens	phenotypic age	phenotypic age	years	whole blood	Illumina 450K | Illumina EPIC	adults	PCA + elastic net regression	78464	2022	497	2026-07-05	Morgan E. Levine	Nature Aging	https://doi.org/10.1038/s43587-022-00248-2	Principal-component DNAm PhenoAge model trained directly on phenotypic-age scores rather than as a proxy of the original CpG clock.			True	Not yet
pcskinandblood	DNA methylation	Homo sapiens	chronological age	skin-and-blood clock output	years	skin | whole blood | cultured fibroblasts	Illumina 450K | Illumina EPIC	all ages	PCA + elastic net regression	78464	2022	497	2026-07-05	Morgan E. Levine	Nature Aging	https://doi.org/10.1038/s43587-022-00248-2	Principal-component proxy of the skin-and-blood age clock, trained against the original Horvath2 score using skin, blood, and fibroblast datasets.		anti_log_linear	True	Not yet
pedbe	DNA methylation	Homo sapiens	chronological age	chronological age	years	buccal epithelium	Illumina 450K | Illumina EPIC	children and adolescents	elastic net regression	94	2020	292	2026-07-05	Michael S. Kobor	Proceedings of the National Academy of Sciences of the United States of America	https://doi.org/10.1073/pnas.1820843116	Pediatric chronological-age estimator developed specifically for noninvasive buccal epithelial-cell samples.		anti_log_linear		Not yet
petkovich	DNA methylation	Mus musculus	chronological age	chronological age	months	whole blood	bisulfite sequencing	mice	elastic net regression	90	2017	441	2026-07-05	Vadim N. Gladyshev	Cell Metabolism	https://doi.org/10.1016/j.cmet.2017.03.016	Mouse blood DNA-methylation age clock built by regression on reduced-representation bisulfite-sequencing CpGs, estimating biological age and shown to be slowed by lifespan-extending interventions such as caloric restriction and dwarfism.		petkovich		Not yet
phenoage	clinical biomarkers	Homo sapiens	phenotypic age	mortality	years	blood	clinical laboratory assays	adults	penalized hazards regression with Gompertz calibration	10	2018	3594	2026-07-05	Steve Horvath	Aging	https://doi.org/10.18632/aging.101414	Clinical Phenotypic Age combines chronological age with nine blood biomarkers selected by penalized mortality regression and expresses mortality risk as an equivalent age in years.		mortality_to_phenoage		Not yet
prostatecancerkirby	DNA methylation	Homo sapiens	prostate cancer	prostate cancer	log odds	prostate	Illumina 450K	adult men	logistic regression	3	2017	61	2026-07-05	Richard M. Myers	BMC Cancer	https://doi.org/10.1186/s12885-017-3252-2	Three-CpG prostate-tissue diagnostic classifier distinguishing malignant from benign-adjacent tissue; it was trained on 73 tumors and 63 benign-adjacent samples and externally validated in TCGA.				Not yet
reedbmi	DNA methylation	Homo sapiens	BMI methylation score	body mass index	unitless	whole blood | cord blood	Illumina 450K	all ages	weighted methylation aggregation	135	2020	86	2026-07-05	Gibran Hemani	Clinical Epigenetics	https://doi.org/10.1186/s13148-020-00841-5	Weighted blood-DNA-methylation score built from published BMI EWAS effect estimates and evaluated across the ARIES life course; it is a biomarker associated with concurrent BMI, not a calibrated prediction in kilograms.				Not yet
replitali	DNA methylation	Homo sapiens	replicative history	population doublings	population doublings	cultured primary human cells	Illumina EPIC	human cell cultures	elastic net regression	87	2022	86	2026-07-05	Peter W. Laird	Nature Communications	https://doi.org/10.1038/s41467-022-34268-8	Final RepliTali model estimating relative cumulative replicative history from methylation in common partially methylated domains; it was fitted to normalized population doublings across serially cultured primary human cells.				Not yet
replitalinorm	DNA methylation	Homo sapiens	replicative history	population doublings	population doublings	cultured fibroblasts	Illumina EPIC	human cell cultures	elastic net regression	218	2022	86	2026-07-05	Peter W. Laird	Nature Communications	https://doi.org/10.1038/s41467-022-34268-8	Upstream starting-PD normalization model used during RepliTali construction. It was trained only in the chronologically youngest fetal skin fibroblast line (AG06561) to estimate the unobserved pre-culture replicative-history offset; it is not the final 87-CpG RepliTali model.				Not yet
retroelementagev1	DNA methylation	Homo sapiens	chronological age	chronological age	years	whole blood	Illumina EPIC	all ages	elastic net regression	1317	2024	26	2026-07-05	Michael J. Corley	Aging Cell	https://doi.org/10.1111/acel.14288	Whole-blood Retroelement-Age V1, trained by 10-fold-cross-validated elastic net on EPIC v1.0 CpGs annotated to HERV and active LINE elements.				Not yet
retroelementagev2	DNA methylation	Homo sapiens	chronological age	chronological age	years	whole blood	Illumina EPIC	all ages	elastic net regression	1378	2024	26	2026-07-05	Michael J. Corley	Aging Cell	https://doi.org/10.1111/acel.14288	Composite Retroelement-Age V2 extends the retroelement annotation to CpGs compatible across EPIC v1.0 and v2.0 and was trained by 10-fold-cross-validated elastic net.				Not yet
senchronoage	DNA methylation	Homo sapiens	chronological age	chronological age	years	whole blood	Illumina 450K | Illumina EPIC	adults	elastic net regression	187	2026	0	2026-07-05	Albert T. Higgins-Chen	Aging Cell	https://doi.org/10.1111/acel.70430	Senescence-enriched chronological-age predictor restricted to CpGs whose directions were concordant across in-vitro senescence, age and mortality analyses.				Not yet
sencultureage	DNA methylation	Homo sapiens	cellular senescence	cellular senescence	log odds	cultured fibroblasts | cultured mesenchymal stromal cells	Illumina 450K | Illumina EPIC	human cell cultures	elastic net logistic regression	142	2026	0	2026-07-05	Albert T. Higgins-Chen	Aging Cell	https://doi.org/10.1111/acel.70430	Binomial elastic-net classifier of in-vitro cellular senescence, trained after ComBat correction on pooled human fibroblast and mesenchymal-stromal-cell datasets and restricted to direction-concordant senescence/age/mortality CpGs.				Not yet
senmortalityage	DNA methylation	Homo sapiens	mortality risk	mortality	log hazard	whole blood	Illumina 450K | Illumina EPIC	adults	elastic net Cox regression	91	2026	0	2026-07-05	Albert T. Higgins-Chen	Aging Cell	https://doi.org/10.1111/acel.70430	Senescence-enriched elastic-net Cox predictor of mortality, restricted to direction-concordant senescence/age/mortality CpGs and trained in the Framingham Heart Study.				Not yet
skinandblood	DNA methylation	Homo sapiens	chronological age	chronological age	years	buccal epithelium | whole blood | epithelium | cultured fibroblasts | skin | cord blood	Illumina 450K | Illumina EPIC	all ages	elastic net regression	391	2018	853	2026-07-05	Kenneth Raj	Aging	https://doi.org/10.18632/aging.101508	Multi-tissue 391-CpG elastic-net chronological-age clock optimized with training data from buccal cells, whole blood, epithelium, fibroblasts, skin and cord blood; it is particularly accurate for skin-derived and cultured cells.		anti_log_linear		Not yet
stemtocvitro	DNA methylation	Homo sapiens	mitotic age	population doublings	beta value	multi-tissue | cultured human cells	Illumina 450K | Illumina EPIC	prenatal and newborn	95th-percentile methylation aggregation	629	2024	24	2026-07-05	Andrew E. Teschendorff	Nature Communications	https://doi.org/10.1038/s41467-024-48649-8	In-vitro precursor of stemTOC based on the 95th percentile across 629 population-doubling-associated CpGs.	0.95 quantile		True	Not yet
stubbs	DNA methylation	Mus musculus	chronological age	chronological age	months	multi-tissue | liver | lung | heart | brain cortex | skeletal muscle | cerebellum | spleen	RRBS	mice	quadratically calibrated elastic net regression	17992	2017	430	2026-07-05	Wolf Reik	Genome Biology	https://doi.org/10.1186/s13059-017-1203-5	Mouse multi-tissue RRBS age predictor: 17,992 common input loci are normalized and reduced to 329 nonzero clock sites, then quadratically calibrated.	quantile_normalization_and_scale_with_gold_standard	stubbs	True	Not yet
systemsage	DNA methylation	Homo sapiens	multisystem biological age	mortality	years	whole blood	Illumina 450K | Illumina EPIC	older adults	PCA + elastic net regression	125175	2025	41	2026-07-05	Morgan Levine	Nature Aging	https://doi.org/10.1038/s43587-025-00958-3	Composite Systems Age integrates the 11 mortality-associated physiological-system scores plus a DNAm chronological-age prediction through PCA and Cox elastic-net regression, then rescales the result to an age-like value.			True	Not yet
systemsageblood	DNA methylation	Homo sapiens	blood-system biological age	mortality	years	whole blood	Illumina 450K | Illumina EPIC	older adults	PCA + elastic net regression	125175	2025	41	2026-07-05	Morgan Levine	Nature Aging	https://doi.org/10.1038/s43587-025-00958-3	Blood-system component of Systems Age: a whole-blood DNAm score built from blood-system biomarker PCs and mortality training, returned on an age-like scale.			True	Not yet
systemsagebrain	DNA methylation	Homo sapiens	brain-system biological age	mortality	years	whole blood	Illumina 450K | Illumina EPIC	older adults	PCA + elastic net regression	125175	2025	41	2026-07-05	Morgan Levine	Nature Aging	https://doi.org/10.1038/s43587-025-00958-3	Brain-system component of Systems Age: a whole-blood DNAm score built from brain-system biomarker and functional PCs and mortality training, returned on an age-like scale.			True	Not yet
systemsageheart	DNA methylation	Homo sapiens	cardiovascular-system biological age	mortality	years	whole blood	Illumina 450K | Illumina EPIC	older adults	PCA + elastic net regression	125175	2025	41	2026-07-05	Morgan Levine	Nature Aging	https://doi.org/10.1038/s43587-025-00958-3	Heart-system component of Systems Age: a whole-blood DNAm score built from cardiovascular-system biomarkers and mortality training, returned on an age-like scale.			True	Not yet
systemsagehormone	DNA methylation	Homo sapiens	endocrine-system biological age	mortality	years	whole blood	Illumina 450K | Illumina EPIC	older adults	PCA + elastic net regression	125175	2025	41	2026-07-05	Morgan Levine	Nature Aging	https://doi.org/10.1038/s43587-025-00958-3	Hormone-system component of Systems Age: a whole-blood DNAm score built from endocrine-system biomarkers and mortality training, returned on an age-like scale.			True	Not yet
systemsageimmune	DNA methylation	Homo sapiens	immune-system biological age	mortality	years	whole blood	Illumina 450K | Illumina EPIC	older adults	PCA + elastic net regression	125175	2025	41	2026-07-05	Morgan Levine	Nature Aging	https://doi.org/10.1038/s43587-025-00958-3	Immune-system component of Systems Age: a whole-blood DNAm score built from immune-system biomarkers and mortality training, returned on an age-like scale.			True	Not yet
systemsageinflammation	DNA methylation	Homo sapiens	inflammatory-system biological age	mortality	years	whole blood	Illumina 450K | Illumina EPIC	older adults	PCA + elastic net regression	125175	2025	41	2026-07-05	Morgan Levine	Nature Aging	https://doi.org/10.1038/s43587-025-00958-3	Inflammation-system component of Systems Age: a whole-blood DNAm score built from inflammatory biomarkers and mortality training, returned on an age-like scale.			True	Not yet
systemsagekidney	DNA methylation	Homo sapiens	renal-system biological age	mortality	years	whole blood	Illumina 450K | Illumina EPIC	older adults	PCA + elastic net regression	125175	2025	41	2026-07-05	Morgan Levine	Nature Aging	https://doi.org/10.1038/s43587-025-00958-3	Kidney-system component of Systems Age: a whole-blood DNAm score built from renal-system biomarkers and mortality training, returned on an age-like scale.			True	Not yet
systemsageliver	DNA methylation	Homo sapiens	hepatic-system biological age	mortality	years	whole blood	Illumina 450K | Illumina EPIC	older adults	PCA + elastic net regression	125175	2025	41	2026-07-05	Morgan Levine	Nature Aging	https://doi.org/10.1038/s43587-025-00958-3	Liver-system component of Systems Age: a whole-blood DNAm score built from hepatic-system biomarkers and mortality training, returned on an age-like scale.			True	Not yet
systemsagelung	DNA methylation	Homo sapiens	pulmonary-system biological age	mortality	years	whole blood	Illumina 450K | Illumina EPIC	older adults	PCA + elastic net regression	125175	2025	41	2026-07-05	Morgan Levine	Nature Aging	https://doi.org/10.1038/s43587-025-00958-3	Lung-system component of Systems Age: a whole-blood DNAm score built from pulmonary-system biomarkers and mortality training, returned on an age-like scale.			True	Not yet
systemsagemetabolic	DNA methylation	Homo sapiens	metabolic-system biological age	mortality	years	whole blood	Illumina 450K | Illumina EPIC	older adults	PCA + elastic net regression	125175	2025	41	2026-07-05	Morgan Levine	Nature Aging	https://doi.org/10.1038/s43587-025-00958-3	Metabolic-system component of Systems Age: a whole-blood DNAm score built from metabolic-system biomarkers and mortality training, returned on an age-like scale.			True	Not yet
systemsagemusculoskeletal	DNA methylation	Homo sapiens	musculoskeletal-system biological age	mortality	years	whole blood	Illumina 450K | Illumina EPIC	older adults	PCA + elastic net regression	125175	2025	41	2026-07-05	Morgan Levine	Nature Aging	https://doi.org/10.1038/s43587-025-00958-3	Musculoskeletal-system component of Systems Age: a whole-blood DNAm score built from musculoskeletal biomarkers and functional measures and mortality training, returned on an age-like scale.			True	Not yet
twelvecelldeconvolutebloodepicbas	DNA methylation	Homo sapiens	basophil proportion	cell-type proportions	proportion	purified blood leukocytes	Illumina EPIC	adults	reference-based constrained deconvolution	240	2022	13	2026-07-05	Brock C. Christensen	Nature Communications	https://doi.org/10.1038/s41467-021-27864-7	Reference-based constrained deconvolution returning the basophil proportion from EPIC-array blood methylation. The published EPIC IDOL-Ext library used 1,200 CpGs selected to optimize recovery of known artificial-mixture cell-type proportions; pyaging instead inherits Biolearn’s undocumented 240-CpG replacement, whose rows reproducibly comprise 10 positive and 10 negative maximal cell-versus-other methylation contrasts per subtype and are not a subset of the published 1,200 probes.	fill_with_reference_means		True	Not yet
twelvecelldeconvolutebloodepicbmem	DNA methylation	Homo sapiens	memory B cell proportion	cell-type proportions	proportion	purified blood leukocytes	Illumina EPIC	adults	reference-based constrained deconvolution	240	2022	13	2026-07-05	Brock C. Christensen	Nature Communications	https://doi.org/10.1038/s41467-021-27864-7	Reference-based constrained deconvolution returning the memory B cell proportion from EPIC-array blood methylation. The published EPIC IDOL-Ext library used 1,200 CpGs selected to optimize recovery of known artificial-mixture cell-type proportions; pyaging instead inherits Biolearn’s undocumented 240-CpG replacement, whose rows reproducibly comprise 10 positive and 10 negative maximal cell-versus-other methylation contrasts per subtype and are not a subset of the published 1,200 probes.	fill_with_reference_means		True	Not yet
twelvecelldeconvolutebloodepicbnv	DNA methylation	Homo sapiens	naive B cell proportion	cell-type proportions	proportion	purified blood leukocytes	Illumina EPIC	adults	reference-based constrained deconvolution	240	2022	13	2026-07-05	Brock C. Christensen	Nature Communications	https://doi.org/10.1038/s41467-021-27864-7	Reference-based constrained deconvolution returning the naive B cell proportion from EPIC-array blood methylation. The published EPIC IDOL-Ext library used 1,200 CpGs selected to optimize recovery of known artificial-mixture cell-type proportions; pyaging instead inherits Biolearn’s undocumented 240-CpG replacement, whose rows reproducibly comprise 10 positive and 10 negative maximal cell-versus-other methylation contrasts per subtype and are not a subset of the published 1,200 probes.	fill_with_reference_means		True	Not yet
twelvecelldeconvolutebloodepiccd4mem	DNA methylation	Homo sapiens	memory CD4+ T cell proportion	cell-type proportions	proportion	purified blood leukocytes	Illumina EPIC	adults	reference-based constrained deconvolution	240	2022	13	2026-07-05	Brock C. Christensen	Nature Communications	https://doi.org/10.1038/s41467-021-27864-7	Reference-based constrained deconvolution returning the memory CD4+ T cell proportion from EPIC-array blood methylation. The published EPIC IDOL-Ext library used 1,200 CpGs selected to optimize recovery of known artificial-mixture cell-type proportions; pyaging instead inherits Biolearn’s undocumented 240-CpG replacement, whose rows reproducibly comprise 10 positive and 10 negative maximal cell-versus-other methylation contrasts per subtype and are not a subset of the published 1,200 probes.	fill_with_reference_means		True	Not yet
twelvecelldeconvolutebloodepiccd4nv	DNA methylation	Homo sapiens	naive CD4+ T cell proportion	cell-type proportions	proportion	purified blood leukocytes	Illumina EPIC	adults	reference-based constrained deconvolution	240	2022	13	2026-07-05	Brock C. Christensen	Nature Communications	https://doi.org/10.1038/s41467-021-27864-7	Reference-based constrained deconvolution returning the naive CD4+ T cell proportion from EPIC-array blood methylation. The published EPIC IDOL-Ext library used 1,200 CpGs selected to optimize recovery of known artificial-mixture cell-type proportions; pyaging instead inherits Biolearn’s undocumented 240-CpG replacement, whose rows reproducibly comprise 10 positive and 10 negative maximal cell-versus-other methylation contrasts per subtype and are not a subset of the published 1,200 probes.	fill_with_reference_means		True	Not yet
twelvecelldeconvolutebloodepiccd8mem	DNA methylation	Homo sapiens	memory CD8+ T cell proportion	cell-type proportions	proportion	purified blood leukocytes	Illumina EPIC	adults	reference-based constrained deconvolution	240	2022	13	2026-07-05	Brock C. Christensen	Nature Communications	https://doi.org/10.1038/s41467-021-27864-7	Reference-based constrained deconvolution returning the memory CD8+ T cell proportion from EPIC-array blood methylation. The published EPIC IDOL-Ext library used 1,200 CpGs selected to optimize recovery of known artificial-mixture cell-type proportions; pyaging instead inherits Biolearn’s undocumented 240-CpG replacement, whose rows reproducibly comprise 10 positive and 10 negative maximal cell-versus-other methylation contrasts per subtype and are not a subset of the published 1,200 probes.	fill_with_reference_means		True	Not yet
twelvecelldeconvolutebloodepiccd8nv	DNA methylation	Homo sapiens	naive CD8+ T cell proportion	cell-type proportions	proportion	purified blood leukocytes	Illumina EPIC	adults	reference-based constrained deconvolution	240	2022	13	2026-07-05	Brock C. Christensen	Nature Communications	https://doi.org/10.1038/s41467-021-27864-7	Reference-based constrained deconvolution returning the naive CD8+ T cell proportion from EPIC-array blood methylation. The published EPIC IDOL-Ext library used 1,200 CpGs selected to optimize recovery of known artificial-mixture cell-type proportions; pyaging instead inherits Biolearn’s undocumented 240-CpG replacement, whose rows reproducibly comprise 10 positive and 10 negative maximal cell-versus-other methylation contrasts per subtype and are not a subset of the published 1,200 probes.	fill_with_reference_means		True	Not yet
twelvecelldeconvolutebloodepiceos	DNA methylation	Homo sapiens	eosinophil proportion	cell-type proportions	proportion	purified blood leukocytes	Illumina EPIC	adults	reference-based constrained deconvolution	240	2022	13	2026-07-05	Brock C. Christensen	Nature Communications	https://doi.org/10.1038/s41467-021-27864-7	Reference-based constrained deconvolution returning the eosinophil proportion from EPIC-array blood methylation. The published EPIC IDOL-Ext library used 1,200 CpGs selected to optimize recovery of known artificial-mixture cell-type proportions; pyaging instead inherits Biolearn’s undocumented 240-CpG replacement, whose rows reproducibly comprise 10 positive and 10 negative maximal cell-versus-other methylation contrasts per subtype and are not a subset of the published 1,200 probes.	fill_with_reference_means		True	Not yet
twelvecelldeconvolutebloodepicmono	DNA methylation	Homo sapiens	monocyte proportion	cell-type proportions	proportion	purified blood leukocytes	Illumina EPIC	adults	reference-based constrained deconvolution	240	2022	13	2026-07-05	Brock C. Christensen	Nature Communications	https://doi.org/10.1038/s41467-021-27864-7	Reference-based constrained deconvolution returning the monocyte proportion from EPIC-array blood methylation. The published EPIC IDOL-Ext library used 1,200 CpGs selected to optimize recovery of known artificial-mixture cell-type proportions; pyaging instead inherits Biolearn’s undocumented 240-CpG replacement, whose rows reproducibly comprise 10 positive and 10 negative maximal cell-versus-other methylation contrasts per subtype and are not a subset of the published 1,200 probes.	fill_with_reference_means		True	Not yet
twelvecelldeconvolutebloodepicneu	DNA methylation	Homo sapiens	neutrophil proportion	cell-type proportions	proportion	purified blood leukocytes	Illumina EPIC	adults	reference-based constrained deconvolution	240	2022	13	2026-07-05	Brock C. Christensen	Nature Communications	https://doi.org/10.1038/s41467-021-27864-7	Reference-based constrained deconvolution returning the neutrophil proportion from EPIC-array blood methylation. The published EPIC IDOL-Ext library used 1,200 CpGs selected to optimize recovery of known artificial-mixture cell-type proportions; pyaging instead inherits Biolearn’s undocumented 240-CpG replacement, whose rows reproducibly comprise 10 positive and 10 negative maximal cell-versus-other methylation contrasts per subtype and are not a subset of the published 1,200 probes.	fill_with_reference_means		True	Not yet
twelvecelldeconvolutebloodepicnk	DNA methylation	Homo sapiens	natural killer cell proportion	cell-type proportions	proportion	purified blood leukocytes	Illumina EPIC	adults	reference-based constrained deconvolution	240	2022	13	2026-07-05	Brock C. Christensen	Nature Communications	https://doi.org/10.1038/s41467-021-27864-7	Reference-based constrained deconvolution returning the natural killer cell proportion from EPIC-array blood methylation. The published EPIC IDOL-Ext library used 1,200 CpGs selected to optimize recovery of known artificial-mixture cell-type proportions; pyaging instead inherits Biolearn’s undocumented 240-CpG replacement, whose rows reproducibly comprise 10 positive and 10 negative maximal cell-versus-other methylation contrasts per subtype and are not a subset of the published 1,200 probes.	fill_with_reference_means		True	Not yet
twelvecelldeconvolutebloodepictreg	DNA methylation	Homo sapiens	regulatory T-cell proportion	cell-type proportions	proportion	purified blood leukocytes	Illumina EPIC	adults	reference-based constrained deconvolution	240	2022	13	2026-07-05	Brock C. Christensen	Nature Communications	https://doi.org/10.1038/s41467-021-27864-7	Reference-based constrained deconvolution returning the regulatory T-cell proportion from EPIC-array blood methylation. The published EPIC IDOL-Ext library used 1,200 CpGs selected to optimize recovery of known artificial-mixture cell-type proportions; pyaging instead inherits Biolearn’s undocumented 240-CpG replacement, whose rows reproducibly comprise 10 positive and 10 negative maximal cell-versus-other methylation contrasts per subtype and are not a subset of the published 1,200 probes.	fill_with_reference_means		True	Not yet
vidalbralo	DNA methylation	Homo sapiens	chronological age	chronological age	years	whole blood	Illumina 27K	adults	linear regression	8	2016	145	2026-07-05	Antonio Gonzalez	Frontiers in Genetics	https://doi.org/10.3389/fgene.2016.00126	Eight-CpG whole-blood chronological-age estimator selected by forward stepwise regression in 390 adults and calibrated by multiple linear regression; CpGs were chosen for compatibility with a single multiplex MS-SNuPE assay.				Not yet
weidner	DNA methylation	Homo sapiens	biological age	chronological age	years	whole blood	Illumina 27K | bisulfite sequencing	adults	linear regression	3	2014	973	2026-07-05	Wolfgang Wagner	Genome Biology	https://doi.org/10.1186/gb-2014-15-2-r24	Three-site whole-blood epigenetic-age estimator. The sites were selected from Illumina 27K blood profiles, and the final multivariate linear equation was fitted on targeted bisulfite-pyrosequencing beta values from 82 blood samples and validated in 69 independent samples.				Not yet
wu	DNA methylation	Homo sapiens	chronological age	chronological age	years	whole blood	Illumina 27K | Illumina 450K	children	screened elastic net regression	111	2019	82	2026-07-05	Huiying Liang	Aging	https://doi.org/10.18632/aging.102399	Child-specific 111-CpG blood age predictor built by sure independence screening followed by elastic net; pyaging converts the published month-scale output to years.		anti_log_linear		Not yet
xchrom	DNA methylation	Homo sapiens	X-chromosome dosage	X-chromosome dosage	unitless	whole blood	Illumina 450K | Illumina EPIC	adults	principal component analysis	4047	2021	38	2026-07-05	Leonard C. Schalkwyk	BMC Genomics	https://doi.org/10.1186/s12864-021-07675-2	X-chromosome first-principal-component score used with the Y score to infer X dosage and classify 46,XX, 46,XY, 45,X, and 47,XXY samples.	sex_estimation_autosomal_zscore		True	Not yet
ychrom	DNA methylation	Homo sapiens	Y-chromosome presence	Y-chromosome presence	unitless	whole blood	Illumina 450K | Illumina EPIC	adults	principal component analysis	284	2021	38	2026-07-05	Leonard C. Schalkwyk	BMC Genomics	https://doi.org/10.1186/s12864-021-07675-2	Y-chromosome first-principal-component score used with the X score to infer Y presence and classify 46,XX, 46,XY, 45,X, and 47,XXY samples.	sex_estimation_autosomal_zscore		True	Not yet
yingadaptage	DNA methylation	Homo sapiens	adaptive epigenetic age	chronological age	years	whole blood	Illumina 450K	adults	causality-weighted elastic net regression	999	2024	183	2026-07-05	Vadim N. Gladyshev	Nature Aging	https://doi.org/10.1038/s43587-023-00557-0	Causality-enriched age predictor restricted to adaptive/protective age-related CpGs, with feature penalties weighted by EWMR causality scores.				Not yet
yingcausage	DNA methylation	Homo sapiens	chronological age	chronological age	years	whole blood	Illumina 450K	adults	causality-weighted elastic net regression	585	2024	183	2026-07-05	Vadim N. Gladyshev	Nature Aging	https://doi.org/10.1038/s43587-023-00557-0	Causality-enriched chronological-age clock using EWMR-prioritized CpGs and feature-specific penalties derived from causality scores.				Not yet
yingdamage	DNA methylation	Homo sapiens	damaging epigenetic age	chronological age	years	whole blood	Illumina 450K	adults	causality-weighted elastic net regression	1089	2024	183	2026-07-05	Vadim N. Gladyshev	Nature Aging	https://doi.org/10.1038/s43587-023-00557-0	Causality-enriched age predictor restricted to damaging age-related CpGs, with feature penalties weighted by EWMR causality scores.				Not yet
zhangblup	DNA methylation	Homo sapiens	chronological age	chronological age	years	whole blood | saliva	Illumina 450K | Illumina EPIC	all ages	best linear unbiased prediction	319607	2019	519	2026-07-05	Peter M. Visscher	Genome Medicine	https://doi.org/10.1186/s13073-019-0667-1	High-dimensional chronological-age predictor using best linear unbiased prediction across the full quality-controlled set of 319,607 methylation probes.	scale_row		True	Not yet
zhangen	DNA methylation	Homo sapiens	chronological age	chronological age	years	whole blood | saliva	Illumina 450K | Illumina EPIC	all ages	elastic net regression	514	2019	519	2026-07-05	Peter M. Visscher	Genome Medicine	https://doi.org/10.1186/s13073-019-0667-1	Elastic-net chronological-age predictor released from the largest multi-cohort training set, using 514 selected CpGs from predominantly blood plus saliva data.	scale_row		True	Not yet
zhangmortality	DNA methylation	Homo sapiens	mortality risk	mortality	unitless	whole blood	Illumina 450K	older adults	weighted linear score	10	2017	404	2026-07-05	Hermann Brenner	Nature Communications	https://doi.org/10.1038/ncomms14617	Ten-CpG whole-blood mortality risk score. Pyaging implements the paper supplement’s continuous LASSO-weighted score exactly (the sum of ten raw beta values multiplied by their published coefficients). The same study also defines a separate simplified 0-10 aberrant-methylation count based on cohort-specific quartile cutoffs.				Not yet